Sea ice characteristics¶
In this notebook, we’ll highlight variables from the ICESat-2 dataset that describe important characteristics of the sea ice: snow depth, ice type, and snow density. We’ll use cartopy and xarray to generate maps and lineplots of the data to demonstrate methods for visualizing the data statically, as opposed to the interactive plotting functions highlighted in the sea ice thickness notebook.
import xarray as xr # For working with gridded climate data
from utils.read_data_utils import read_book_data # Helper function for reading the data from the bucket
from utils.wrangling_utils import restrictRegionally # Region restriction
from utils.plotting_utils import static_winter_comparison_lineplot, staticArcticMaps, interactiveArcticMaps, compute_gridcell_winter_means # Plotting utils
# Plotting dependencies
%config InlineBackend.figure_format = 'retina'
import matplotlib as mpl
mpl.rcParams['figure.dpi'] = 150 # Sets figure size in the notebook
# Remove warnings to improve display
import warnings
warnings.filterwarnings('ignore')
1) Read in the data¶
book_ds = read_book_data() # Read/download the data
book_ds = restrictRegionally(book_ds, regionKeyList=[10,11,12,13,15]) # Restrict data to the Inner Arctic
years = [2018,2019,2020] # Years over which to perform analysis
Regions selected: Inner Arctic
2) Map monthly data¶
Here, we’ll use the interactiveArcticMaps function to display the data. You can change the variable to display by changing data_var in the code cell below if you run the notebook in Binder.
data_var = "freeboard_int"
interactiveArcticMaps(book_ds[data_var], frame_width=450)